"""simple docstring"""



import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()






class  snake_case__      (       snake_case_,						snake_case_,						unittest.TestCase       ):
 _snake_case       :					Any							  =						VQModel
 _snake_case       :					List[Any]							  =						"""sample"""


 @property
 def 						a__   ( self    ,		lowerCamelCase=(32, 32)						):
        __a       =    4
        __a       =    3

        __a       =    floats_tensor((batch_size, num_channels) + sizes						).to(lowerCamelCase						)

        return {"sample": image}


 @property
 def 						a__   ( self						):
        return (3, 32, 32)


 @property
 def 						a__   ( self						):
        return (3, 32, 32)


 def 						a__   ( self						):
        __a       =    {
            "block_out_channels": [32, 64],
            "in_channels": 3,
            "out_channels": 3,
            "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
            "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
            "latent_channels": 3,
        }
        __a       =    self.dummy_input
        return init_dict, inputs_dict


 def 						a__   ( self						):
        pass


 def 						a__   ( self						):
        pass


 def 						a__   ( self						):
        __a ,   __a       =    VQModel.from_pretrained("fusing/vqgan-dummy"    ,		output_loading_info=lowerCamelCase						)
        self.assertIsNotNone(lowerCamelCase						)
        self.assertEqual(len(loading_info["missing_keys"]						)    ,		0						)

        model.to(lowerCamelCase						)
        __a       =    model(**self.dummy_input						)

        assert image is not None, "Make sure output is not None"






 def 						a__   ( self						):
        __a       =    VQModel.from_pretrained("fusing/vqgan-dummy"						)
        model.to(lowerCamelCase						).eval()

        torch.manual_seed(0						)
        if torch.cuda.is_available():
               torch.cuda.manual_seed_all(0						)

        __a       =    torch.randn(1    ,		model.config.in_channels    ,		model.config.sample_size    ,		model.config.sample_size						)
        __a       =    image.to(lowerCamelCase						)
        with torch.no_grad():
               __a       =    model(lowerCamelCase						).sample

        __a       =    output[0, -1, -3:, -3:].flatten().cpu()
        # fmt: off
        __a       =    torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]						)
        # fmt: on
        self.assertTrue(torch.allclose(lowerCamelCase    ,		lowerCamelCase    ,		atol=1E-3						)						)

"""simple docstring"""



import os



def 	_lowerCamelCase(	a = "matrix.txt"				):
       with open(os.path.join(os.path.dirname(a				)   ,    a				)				) as in_file:
              __a       =    in_file.read()

       __a       =    [[int(a				) for cell in row.split(","				)] for row in data.strip().splitlines()]
       __a       =    [[0 for cell in row] for row in grid]
       __a       =    len(grid[0]				)

       __a       =    [[0 for i in range(a				)] for j in range(a				)]
       __a       =    grid[0][0]
       for i in range(1   ,    a				):
              __a       =    grid[0][i] + dp[0][i - 1]
       for i in range(1   ,    a				):
              __a       =    grid[i][0] + dp[i - 1][0]

       for i in range(1   ,    a				):
              for j in range(1   ,    a				):
                     __a       =    grid[i][j] + min(dp[i - 1][j]   ,    dp[i][j - 1]				)

       return dp[-1][-1]


if __name__ == "__main__":
   print(F'''{solution() = }''')
